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Metro passenger’s path choice model estimation with travel time correlations derived from smart card data

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  • Yongsheng Zhang
  • Enjian Yao
  • Kangning Zheng
  • Hao Xu

Abstract

Smart card data provides a new perspective for estimating a metro passenger’s path choice model in a large-scale urban rail transit network with multiple alternative paths between origin-destination pairs. However, existing research does not consider correlations of path travel times among alternative paths when using smart card data for estimation purposes, leading to biased estimations. This paper proposes an approach to estimating the path choice model considering path travel time correlations. In particular, a simplified form of measuring path travel time correlations caused by shared links is proposed to improve estimation efficiency. Then a framework for a linking path choice model and smart card data is developed based on a Gaussian mixture model; an expectation maximization-based estimation algorithm is also provided. Finally, taking the Guangzhou Metro in China as an example, the superiority of estimations based on smart card data considering correlations is observed in both statistical terms and predictions.

Suggested Citation

  • Yongsheng Zhang & Enjian Yao & Kangning Zheng & Hao Xu, 2020. "Metro passenger’s path choice model estimation with travel time correlations derived from smart card data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(2), pages 141-157, February.
    • Handle: RePEc:taf:transp:v:43:y:2020:i:2:p:141-157
    DOI: 10.1080/03081060.2020.1717135
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    Cited by:

    1. Wang, Ying & Zhao, Ou & Zhang, Limao, 2024. "Modeling urban rail transit system resilience under natural disasters: A two-layer network framework based on link flow," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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